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Smart Storage Sizing
Storage intelligence
Getting the best performance out of your SVC, V7000, DS5000
and DS8000
Dr. Gilbert HoutekamerIntelliMagic BV
Storage Intelligence 2
IntelliMagic Overview
A world leader in Storage Performance Management software solutions
Developing SPM solutions since 1991
Private, no debt
Headquarters in Leiden, NLUS office in Dallas, TX
Storage Intelligence 3
Overview
Introduction to Storage Performance Management
Monitoring Storage Performance with SMI-S
Interpreting Storage Performance Data
IntelliMagic Vision examples for IBM Storage
IntelliMagic Vision and Direction Product Summaries
Storage Intelligence 4
Storage Performance ManagementIntroduction
© IntelliMagic, 2011
Storage Intelligence 5
Storage Performance Management (SPM)Definition
Storage Performance Management is the process of ensuring• that users constantly receive required I/O service levels to
avoid performance problems; • that storage assets are efficiently configured and used to
avoid over spending on hardware.
Risk avoidance is more important than saving money.
Storage Intelligence
Over configured: Efficiencyissues
6
Sweet spot:Right performanceRight Cost
size (and cost) of storage configuration
cost
performance
Utilization levels
Underconfigured: Performance issues
lower
higher
Risk is notremoved: Hot spots will still occur from time due to imbalance
The cost difference between these two is often 330% or
more of all storage
Risks
Storage Intelligence 7
SPM Maturity Stages
Often 30% or more difference in total hardware costs
Storage Intelligence
SPM Drivers - How did we get here? Storage Architecture Evolution
Why are most sites at reactive maturity stages?
Answer: Queuing no longer happens on the host, but inside the storage system where you can’t see what is going on.
8
The Big Shift: The host is rarely I/O constrained anymore – today’s bottlenecks are with specific components inside the storage system
… …
DA DA
DA DAHA
HA
HA
HA
HA
HA
SMP
“Visibility Gap”
Storage Intelligence
Visibility Gap: Looking Inside
To be effective, an SPM solution must:1. Provide visibility into workload measurements inside the
storage system where the bottlenecks occur, and 2. Correlate the workload metrics with the capabilities of
specific hardware components in use.
In particular, visibility of the hardware components:on the front-end (host adapters) and on the back end (the physical devices)
SMI-S data provides this visibility
Storage Intelligence
Front-end and Back-end
© IntelliMagic 2011 10
Storage system performance measurements need to include:
Cache
Front-end Adapter
Front-end Adapter
Back-end Adapter
Back-end Adapter
Disks
Logical Write
Logical Read
Physical Write
Physical Read
Storage Intelligence
Logical and Physical I/Os
Logical I/Os from Servers to LUNs on front-end ports
Physical I/Os to Back-end drives
Large caches reduce read-misses and buffer writes
Sequential I/O always implies back-end I/O
11© IntelliMagic 2011
Back-end array groups
Read hit Read missRandom
WriteSeq.
Write
Storage Intelligence 12
Monitoring Storage Performance with SMI-S
Storage Intelligence
Server Performance Data
© IntelliMagic 2011 13
linux iostat system=LG_ABC20 interval=600 01oct2010 14:43
avg-cpu: %user %nice %sys %iowait %idle6.54 0.24 1.53 3.41 88.28
Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s rkB/s wkB/s avgrq-sz avgqu-sz await svctm %utilsda 4.97 10.12 0.84 6.84 31.01 78.23 15.50 39.12 14.23 0.00 0.19 0.45 0.35sda1 0.58 0.00 0.03 0.00 1.21 0.00 0.60 0.00 47.45 0.00 4.57 1.67 0.00sda2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 0.00 1.33 1.33 0.00sda5 2.48 1.05 0.62 1.12 24.76 17.43 12.38 8.72 24.20 0.00 3.12 1.97 0.34sda6 0.01 2.79 0.00 1.39 0.12 33.45 0.06 16.73 24.05 0.00 1.91 0.47 0.07sda7 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.85 0.00 21.78 14.86 0.00sda8 0.11 0.28 0.01 0.69 0.97 7.78 0.48 3.89 12.39 0.00 0.50 4.46 0.32sda9 0.10 1.72 0.01 1.44 0.23 6.33 0.12 3.17 4.52 0.00 2.12 0.13 0.02sda10 1.69 4.28 0.16 2.19 3.72 13.23 1.86 6.61 7.20 0.00 1.68 0.67 0.16sda11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.51 0.00 8.99 8.84 0.00sda12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10.00 0.00 0.83 0.83 0.00sdb 13.66 25.95 4.56 15.78 145.72 344.50 72.86 172.25 24.10 0.00 0.21 0.09 0.17sdb1 13.66 25.95 4.56 15.78 145.72 344.50 72.86 172.25 24.10 0.00 0.21 0.09 0.17
Most UNIX and Windows servers do have end to end response times and disk transfers/sec available for each LUN that can be used for identifying if a disk problem exists.
Unfortunately they do not have the insight into the backend storage components required to identify root cause of backend storage issues.
Storage Intelligence
Storage Performance Data
© IntelliMagic 2011 14
Storage system performance measurements need to include:
Cache
Front-end Adapter
Front-end Adapter
Back-end Adapter
Back-end Adapter
Disks
Logical Write
Logical Read
Physical Write
Physical Read
Storage Intelligence
SMI-S Defines Components that are monitored
ElementType Component Vendor Specific
ElementType2 Cumulative statistics for the storage system
ElementType3 Front-end Controllers
ElementType4 Peer Storage System (Mirroring)
ElementType5 Back-end Controllers
ElementType6 Front-end FC ports
ElementType7 Back-end Ports
ElementType8 Volumes
ElementType9 Extent – Intermediate storage
ElementType10 Disk Drive
ElementType11 Arbitrary Logical Units – Controller commands
ElementType12 Remote Replica Group – Remote Mirror
15
Same element types are used by all vendors, although the statistics collected to differ. In general, Enterprise Storage Systems from IBM and EMC collect more detail than the midrange systems
Storage Intelligence
SMI-S allows for vendor extensions
© IntelliMagic 2011
16
IBMSpecific
EMC SpecificOther
vendors
SMI-Sminimu
m set
SMI-S Standard
First time ever to have storage performancedata across vendors in generic standard.
Response time for DS5000 is an example of an extension
Note that TPC only supports SMI-S extensions for DS8000 and SVC
Storage Intelligence
SMI-S Data Collection Comparison(including IntelliMagic Vision enriched data)
Component
IBM EMC HDS
DS8X00 DS5000 V7000 SVC DMXVMAX
CLARiiON
VNX(SAN)
USP VVSP
Top Level Computer System
Front-end Adapter
Peer-to-peer Back-end Adapter
Front-end Ports 1 1
Back-end Ports Volume
Storage Pool/RankDisk Drive
Replication2
17
1 For SVC/V7000 some of the information is obtained from XML files 2 DS8000 replication link usage, DMX/VMAX SRDF/A usage, CLARiiON Snap Sessions
Storage Intelligence
DS5000 Architecture Overviewwith SMI-S representation
© IntelliMagic 2011 18
SMI-S: Top Level
Computer System
Front-end Adapter
Front-end Port
Disk Drive
volumes
Not every item from the
hardware is represented in
an SMI-S concept.
Storage Intelligence
Not only Performance Data
SMI-S is a rich source of configuration data• Which LUNs are defined on which extent pool• Which physical drives make up an array group• Which port (types) are connected to each (host adapter)
19
Disks
Extent/Raid Group
Volume
Front End
Storage Intelligence
SMI-S: Cross-Vendor Storage-Centric Measurements
SMI-S is standard like SNMP, supported by IBM, EMC, Hitachi/HP etcThe SMI-S provider communicates with hardware to publish data.Implementations vary, eg separate provider for EMC Clariion or embedded for EMC V-Max or IBM DS8800Detailed information on at least logical and physical volumesUsually more information eg ports, component utilization, even replication
Storage Intelligence
SPM Technology: Vendor Specific Versus Vendor Neutral
© IntelliMagic 2011 21
Feature Vendor Specific SMI-S (Vendor Neutral)Communication between tool and storage device
Vendor proprietary fast protocol
Standard XML based communication for all exchanges
Support for platform specific metrics
No distinction between standard and platform specific metrics
Requires minimum number of fields, but supports vendor extensions for extra fields and components
Compatibility with other hardware
Specific to one hardware platform
Any hardware supporting SMI-S
Third party access
Few documented interfaces, different interfaces for each platform
Open specification to any tool vendor
Provisioning SupportDesigned for specific hardware
Difficult to support with SMI-S due to hardware specific implementations
Storage Intelligence 22
Interpreting Storage Performance Data
Storage Intelligence
IntelliMagic Vision Data Base
Repository with storage performance data (DB2 or SQL Server)
Daily automated Health Check & Dashboard for quick assessment
All data for performance problem diagnosis readily available in case of problems
Tracking of early warning information will avoid future application performance issues
Storage Intelligence
How Do I Know if Any DSS’s Have a Problem?
© IntelliMagic 2011 24Red – Bad!
DS4800
Storage Intelligence
Match Hardware Capability to KPIs and Measurement
© IntelliMagic 2011 25
DS4800
Storage Intelligence
DSS SLA for Disk Response Time
© IntelliMagic 2011 26
DS5000DS8000
Storage Intelligence
Host Adapter Utilization
© IntelliMagic 2011 27
DS5300
Storage Intelligence
SPM Technology: DSS -> RAID Group Drill Down
© IntelliMagic 2010 28
DS8000
Storage Intelligence
SPM Technology: RAID Group to Volume Drill Down
© IntelliMagic 2010 29
DS8000
Storage Intelligence
SPM Technology: Time Normalization
© IntelliMagic 2011 30
Storage Intelligence
I/O Rate vs. Response TimeExamples based on SMI-S data
© IntelliMagic 2011 31
Correlating I/O rate and response time doesn’t really tell us anything
Storage Intelligence
HDD Response Service Levels
© IntelliMagic 2011 32
Interpreted data from SMI-S
DS8000
Storage Intelligence
I/O Rate and Response Time Correlation
© IntelliMagic 2011 33
Response time (as seen by the application on Fibre ports)
Lack of correlation (20:30 – 22:30) indicates possible DSS component over utilization
DS8000
Storage Intelligence
Cache Over Utilization?
© IntelliMagic 2011 34
FW Bypass are I/O operations that are delayed because of lack of write cache resources in the disk storage system.DS8000
Storage Intelligence
Closer look at Extent Pool 25
© IntelliMagic 2011 35
Cache bypass is not significant when viewed at a DSS level, but comprises approximately 45% of write I/Os for this extent pool!
FW bypass seriously affects I/O response timeDS8000
Storage Intelligence
SVC Back-End
Storage Intelligence
Storage Intelligence
Storage Intelligence
Configuration drill-down
SVC Configuration
Storage Intelligence
How is IntelliMagic Vision implemented
Storage Intelligence
IntelliMagic Vision Architecture (Open)
41
IntelliMagicVision
SMI Providers
Arrays
Windows
Vision SMI-S Collector
Data Store
DB2
SQL Server
Storage Intelligence
Installation process
ProcessIntelliMagic Consulting servicesRequirement analysisDeterminen type of reporting
Implementation stepsSet up SMI-S agentsDaily reporting run on last loadWeekly/Monthly reporting run on trending data base
Separate version for z/OS usersRMF/SMF data input
Storage Intelligence
PDF Export
Reporter:Indexed PDF for
standardand
user report sets
Storage Intelligence
HTML Export
Dashboard, Charts,Tables
Storage Intelligence
PPT Export
Annotated PowerPoint
Classic Reporter andReporter
All charts
Ready to publish or present
Storage Intelligence
Raw data in CSV format
Raw data for further analysis
Storage Intelligence 47
IntelliMagic Direction
© IntelliMagic, 2011
Storage Intelligence © IntelliMagic 2011 48
SPM Technology - Predictive Process Requirements
Modeling
Design-ing Tiers
Data Layout
What if we grow or change hardware?
What is the best tier for each application
How can I optimizemy volume layout?
Storage Intelligence © IntelliMagic 2011
Direction Overview
Files with workload measurements from the Servers and/or the DSS’s
Set Base Line:Set reference pointReport on utilizations
Predict effects of:Configuration changesWorkload growthCache behaviorMigrationsConsolidation
Tabular andGraphical output
Disk Subsystem Configuration
input
Direction
Cache
FED FED
BED BED
Storage Intelligence
Supports All Mainline Hardware and Software Platforms
DMC
UNIX / AIX LINUX IOSTAT
I5/OS PT Reports
Windows Perfmon
IBM Disk Subsystem TPC
DirectionIBM
EMC
DSS, SMI-S & Vision CSV
MFO
HDSHP
MFO = Multi-File OpenDMC = Disk Magic Control
PT = Performance ToolTPC = TotalStorage Productivity Center
z/OS RMFIntelliMagic
Vision
Storage Intelligence © IntelliMagic 2011 51
Forecasting Performance Requirements and Modeling
Workloads
Storage Intelligence
SVC Utilizations Inside
© IntelliMagic 2011 52
Storage Intelligence
How are the utilizations of the Backend Controllers?
Utilizations inside the DS5300.
What happens inside the backend DS8100?
53© IntelliMagic 2011
Storage Intelligence
What Happens to Utilization if We Add Additional an HBA to DS5300?
© IntelliMagic 2011 54
HA Utilization drops!
Storage Intelligence
What Happens to HA Utilization with Additional HBAs on DS8100?
© IntelliMagic 2011 55
HA Utilization drops!
Storage Intelligence 56
Thank YouQuestions?
© IntelliMagic, 2011